Senior Deep Learning Software Engineer – Inference

NVIDIA

. Design, build, and optimize GPU-accelerated software powering AI applications .

Posted 10/12/2026full-timeUnited StatesSenior💰 $152,000 - $287,500 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing GPU-accelerated software for AI applications, with a strong focus on deep learning model performance and inference optimization. Proficient in C/C++ programming, CUDA, and contributing to open-source deep learning frameworks.

Highest-signal resume keywords

GPU Programming (CUDA, OAI Triton, CUTLASS)C/C++ Programming and Software DesignDeep Learning Model OptimizationExperience with Multi GPU Communications (NCCL, NVSHMEM)Contributing to Deep Learning Software Projects (PyTorch, vLLM, SGLang)

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Hard Skills

C/C++ ProgrammingCUDA ProgrammingDeep Learning Model OptimizationPerformance ModelingProfilingDebuggingCode OptimizationSoftware DevelopmentAI Application DevelopmentInference Frameworks

Soft Skills

CollaborationAgile Methodologies

Tools & Technologies

NVIDIA AcceleratorsCUTLASSOAI TritonNCCLVLLMSGLangFlashInferDeep Learning Frameworks

Certifications & Qualifications

Masters or PhD in Computer Engineering, Computer Science, EECS, or AI

Industry Keywords

GPU-Accelerated SoftwareAI ApplicationsLarge-Scale Model ServingGenerative AILanguage ModelsInference Optimization

Tech Stack

Tools & technologies

PythonPyTorchC++

About the role

Key responsibilities & impact

Design, build, and optimize GPU-accelerated software powering AI applications
Develop and maintain high-performance open-source frameworks for large-scale model serving and inference
Improve inference platforms and facilitate deployment and serving of language models
Implement the latest algorithms for public release in inference frameworks
Identify and drive performance improvements for state-of-the-art LLM and Generative AI models across NVIDIA accelerators
Use open-source tools and plugins, including CUTLASS, OAI Triton, NCCL, and CUDA kernels, to implement and optimize model serving pipelines
Optimize, analyze, and tune deep learning models in LLM, Multimodal, and Generative AI domains
Scale deep learning model performance across different architectures and NVIDIA accelerator types
Contribute features and code to NVIDIA’s inference libraries, vLLM, SGLang, FlashInfer, and LLM software solutions
Collaborate with teams across frameworks, NVIDIA libraries, and inference optimization solutions

Requirements

What you’ll need

Masters or PhD or equivalent experience in relevant field (Computer Engineering, Computer Science, EECS, AI)
5+ years of relevant software development experience
Excellent C/C++ programming and software design skills
SW Agile skills are helpful
Python experience is a plus
Prior experience with training, deploying or optimizing the inference of DL models in production is a plus
Prior background with performance modeling, profiling, debug, and code optimization or architectural knowledge of CPU and GPU is a plus
GPU programming experience (CUDA, OAI TRITON or CUTLASS) is a plus
Experience contributing to deep learning software projects, such as PyTorch, vLLM, and SGLang
Experience with Multi GPU Communications (NCCL, NVSHMEM)

Benefits

Comp & perks

Comprehensive benefits package
Equity